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Causes of death


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GHME 2013 Conference
Session: Global Burden of Diseases, Injuries, and Risk Factors Study 2010: workshop on methods and key findings
Date: June 18 2013
Presenter: Rafael Lozano
Institute for Health Metrics and Evaluation (IHME), University of Washington

Published in: Health & Medicine, Technology
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Causes of death

  1. 1. Causes of death Rafael Lozano, MD. Director of the Center for Health System Research, NIPH, Mexico Director of the Latin America and the Caribbean initiatives, IHME. Global Burden of Diseases, Injuries, and Risk Factors Study 2010: workshop on methods and key findings June 18, 2013
  2. 2. Outline • Background • Methods: Main Issues for calculating causes of death • Key findings • Conclusions 2
  3. 3. Background • Causes of death (CoD) is one of the most fundamental metrics for population health. • Trends in CoD provide an important summary of whether society is or is not making progress in reducing burden of premature mortality and especially avoidable mortality. • Usually CoD assessments show success and failures of Health Information Systems and provide directions of how to improve them. • GBD 1990 was the first comprehensive study to present the global leading causes of death. 3
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  5. 5. Global cause of death assessment: main issues • The universe of data • Efforts to assess and enhance quality and comparability of data • The statistical modeling strategy • Causes of death constrained to sum to all cause mortality 5
  6. 6. The universe of CoD data We have to identify all available data on causes of death for all countries for a period of time We have to identify all different sources of CoD data • We collected data on around 600 million deaths in the last 30 years • Data available varies by disease: o More on maternal, cancer, injuries o Less on NTD, diarrhea and LRI pathogens 6 Only VR All Sources
  7. 7. Data Sources, Countries, and Site years, 1900-2010 7
  8. 8. Assessment and enhancement of data quality and comparability 1. Assessment of completeness 2. Causes of death mapping 3. Redistribution of misclassified causes of death 4. Age and age-sex splitting 5. Smoothing for stochastic variation due to small numbers 6. Outlier detection 8
  9. 9. Mapping one cause in different ICD 9
  10. 10. Mapping the cause of death lists 10 BTL1 2 3 4 5 Tab B 6,7 Tab A 8 9 tab 9 VA 10 Tab 10 Different type of ICD 1900 2010 SPC countries map Different type of ICD GBD 1990 Cause List (100) GBD 2010 Cause List (235) 24 causes 39 causes
  11. 11. 11 Group A: Communicable, maternal, perinatal and nutritional conditions D. Intestinal infectious diseases 1. Diarrheal disease Group B: Non communicable diseases H. Cardiovascular and circulatory diseases Group C: Injuries A. Unintentional injuries a .Cholera c. Shigellosis i. Rotaviral enteritis 4. Cerebrovascular disease b. Hemorrhagic stroke 1. Transport Injuries a. Road injury a3. injury - motorized two-wheeler rider GBD2010 has a list of 235 CoD Using ICD9th and ICD10th revisions Top down hierarchical map
  12. 12. 12 Percent of Garbage Codes by type, and sex in all ICD10 ,IHME data set (26.6%)
  13. 13. 13
  14. 14. Garbage Codes from ICD10 (Percent of deaths 14
  15. 15. Redistribution of Garbage Codes 15 Garbage Codes C26 ill-defined Cancers for digestive organs Target Codes C16 Stomach cancer C17 Cancer of the small intestine C18 Colon cancer C19 Malignant neoplasm of rectosigmoid junction C23 Gallbladder cancer C24 Malignant neoplasm of other parts of biliary tract C25 Pancreatic cancer
  16. 16. Modeling causes of death 1. Causes of death ensemble modeling, CODEm (133 causes), including all major causes except HIV. CODEm selects models and ensembles of models based on out-of-sample performance. 2. Negative binomial (12 causes). 3. Fixed proportion models (27 causes). 4. Disaggregation by pathogens or sub-causes (36 causes). 5. Natural history models (8 causes). 6. Mortality shock regressions (2 causes). 16
  17. 17. Combining results: CoDCorrect algorithm • Because we developed single-cause models, it was imperative as a final step to ensure that individual cause estimates summed to the all-cause mortality estimate for every age-sex- country-year group. • This is one of the innovations of this study: o Implemented taking into account uncertainty in every cause of death model outcome o We proportionately rescaled every cause such that the sum of the cause-specific estimates equaled the number of deaths from all causes generated from the demographic analysis (by country, year, age, and sex). o We applied CoDCorrect in a hierarchical way. 17
  18. 18. Shift of causes of death in the last 20 years 18 Comm/Mater/ Neonat/Nutr 34% Injuries 9% Comm/Mater /Neonat/Nutr 25% Injuries 10% 1990 2010 Non Communicable Diseases 57% Non Communicable Diseases 65% 46.5 million 52.7 million
  19. 19. Death decomposition analysis by changes in population 19 % change 1990-2000 % change due to change in rates % change due to pop ageing % change due to pop growth -75% -50% -25% 0% 25% 50% 75% All Causes Com/Mat Neo/Nut NCD Injuries
  20. 20. Percentage of global deaths for female and male individuals in 2010 by cause and age Males Females
  21. 21. 21 Rapid shifts in leading causes of global death
  22. 22. Percentage of YLLs for all ages and both sexes combined by cause and region in 1990 and 2010 1990 2010
  23. 23. 23
  24. 24. Key findings • The shifting pattern of the number of deaths by cause across time, countries, and age groups is consistent with the three key drivers of change • Despite the important epidemiological shift in the world, the MDGs related deaths in Sub Sahara Africa represent 60% of all deaths in that region during 2010 • New set of analytical approaches and methods: o Improved diagnostic redistribution o The modeling strategy depends of the strength of available data: CODEM and CoD Correct are both innovations in the field • Adding time trends and quantifying the uncertainty differentiate GBD 2010 from similar studies in the past, however without correction of known bias, comparability is impossible. 24